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Boosting the performance of the fuzzy min-max neural network in pattern classification tasks

journal contribution
posted on 2006-01-01, 00:00 authored by K Chen, Chee Peng Lim, R Harrison
In this paper, a boosted Fuzzy Min-Max Neural Network (FMM) is proposed. While FMM is a learning algorithm which is able to learn new classes and to refine existing classes incrementally, boosting is a general method for improving accuracy of any learning algorithm. In this work, AdaBoost is applied to improve the performance of FMM when its classification results deteriorate from a perfect score. Two benchmark databases are used to assess the applicability of boosted FMM, and the results are compared with those from other approaches. In addition, a medical diagnosis task is employed to assess the effectiveness of boosted FMM in a real application. All the experimental results consistently demonstrate that the performance of FMM can be considerably improved when boosting is deployed.

History

Journal

Advances in soft computing

Volume

34

Pagination

373 - 387

Publisher

Springer

Location

Berlin, Germany

ISSN

1615-3871

Language

eng

Notes

This paper was presented at the 9th Online World Conference on Soft Computing in Industrial Applications 2004.

Publication classification

C1.1 Refereed article in a scholarly journal

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